



'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__          =  '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'




def 							lowerCamelCase	(					_snake_case	:    Tuple  ,_snake_case	:    str  ,_snake_case	:    str=8							):





    '''simple docstring'''


    lowercase__									=    height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    lowercase__									=    width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor




def 							lowerCamelCase	(					_snake_case	:    Optional[int]  ,_snake_case	:    Optional[Any]=512  ,_snake_case	:    List[str]=512							):





    '''simple docstring'''


    lowercase__									=    pil_image.resize((w, h)  ,resample=Image.BICUBIC  ,reducing_gap=1							)
    lowercase__									=    np.array(pil_image.convert("RGB"							)							)
    lowercase__									=    arr.astype(np.floataa							) / 127.5 - 1
    lowercase__									=    np.transpose(_A  ,[2, 0, 1]							)
    lowercase__									=    torch.from_numpy(_A							).unsqueeze(0							)
    return image



class 					snake_case			(lowerCamelCase__ ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,)	->				Union[str, Any]:
           super().__init__()

           self.register_modules(
               unet=__snake_case  ,scheduler=__snake_case  ,movq=__snake_case  ,)
           lowercase__									=    2 ** (len(self.movq.config.block_out_channels   ) - 1)



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Optional[Any]:
           # get the original timestep using init_timestep
           lowercase__									=    min(int(num_inference_steps * strength   )  ,__snake_case   )

           lowercase__									=    max(num_inference_steps - init_timestep  ,0   )
           lowercase__									=    self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None   )	->				str:
           if not isinstance(__snake_case  ,(torch.Tensor, PIL.Image.Image, list)   ):
               raise ValueError(
                   F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case   )}'''   )

           lowercase__									=    image.to(device=__snake_case  ,dtype=__snake_case   )

           lowercase__									=    batch_size * num_images_per_prompt

           if image.shape[1] == 4:
               lowercase__									=    image

           else:
               if isinstance(__snake_case  ,__snake_case   ) and len(__snake_case   ) != batch_size:
                   raise ValueError(
                       F'''You have passed a list of generators of length {len(__snake_case   )}, but requested an effective batch'''
                       F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

               elif isinstance(__snake_case  ,__snake_case   ):
                   lowercase__									=    [
                       self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(__snake_case   )
                   ]
                   lowercase__									=    torch.cat(__snake_case  ,dim=0   )
               else:
                   lowercase__									=    self.movq.encode(__snake_case   ).latent_dist.sample(__snake_case   )

               lowercase__									=    self.movq.config.scaling_factor * init_latents

           lowercase__									=    torch.cat([init_latents]  ,dim=0   )

           lowercase__									=    init_latents.shape
           lowercase__									=    randn_tensor(__snake_case  ,generator=__snake_case  ,device=__snake_case  ,dtype=__snake_case   )

           # get latents
           lowercase__									=    self.scheduler.add_noise(__snake_case  ,__snake_case  ,__snake_case   )

           lowercase__									=    init_latents

           return latents



       def 				_a   (					self  ,UpperCAmelCase_=0   )	->				Any:
           if is_accelerate_available():
               from accelerate import cpu_offload
           else:
               raise ImportError("Please install accelerate via `pip install accelerate`"   )

           lowercase__									=    torch.device(F'''cuda:{gpu_id}'''   )

           lowercase__									=    [
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(__snake_case  ,__snake_case   )



       def 				_a   (					self  ,UpperCAmelCase_=0   )	->				Tuple:
           if is_accelerate_available() and is_accelerate_version(">="  ,"0.17.0.dev0"   ):
               from accelerate import cpu_offload_with_hook
           else:
               raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."   )

           lowercase__									=    torch.device(F'''cuda:{gpu_id}'''   )

           if self.device.type != "cpu":
               self.to("cpu"  ,silence_dtype_warnings=__snake_case   )
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           lowercase__									=    None
           for cpu_offloaded_model in [self.unet, self.movq]:
               lowercase__									=    cpu_offload_with_hook(__snake_case  ,__snake_case  ,prev_module_hook=__snake_case   )

           # We'll offload the last model manually.
           lowercase__									=    hook



       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 				_a   (					self   )	->				Dict:
           if not hasattr(self.unet  ,"_hf_hook"   ):
               return self.device
           for module in self.unet.modules():
               if (
                   hasattr(__snake_case  ,"_hf_hook"   )
                   and hasattr(module._hf_hook  ,"execution_device"   )
                   and module._hf_hook.execution_device is not None
               ):
                   return torch.device(module._hf_hook.execution_device   )
           return self.device







       @torch.no_grad()
       @replace_example_docstring(__snake_case   )
       def __call__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = 512  ,UpperCAmelCase_ = 512  ,UpperCAmelCase_ = 100  ,UpperCAmelCase_ = 4.0  ,UpperCAmelCase_ = 0.3  ,UpperCAmelCase_ = 1  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = "pil"  ,UpperCAmelCase_ = True  ,)	->				Optional[int]:
           lowercase__									=    self._execution_device

           lowercase__									=    guidance_scale > 1.0

           if isinstance(__snake_case  ,__snake_case   ):
               lowercase__									=    torch.cat(__snake_case  ,dim=0   )
           lowercase__									=    image_embeds.shape[0]
           if isinstance(__snake_case  ,__snake_case   ):
               lowercase__									=    torch.cat(__snake_case  ,dim=0   )

           if do_classifier_free_guidance:
               lowercase__									=    image_embeds.repeat_interleave(__snake_case  ,dim=0   )
               lowercase__									=    negative_image_embeds.repeat_interleave(__snake_case  ,dim=0   )

           lowercase__									=    torch.cat([negative_image_embeds, image_embeds]  ,dim=0   ).to(dtype=self.unet.dtype  ,device=__snake_case   )

           if not isinstance(__snake_case  ,__snake_case   ):
               lowercase__									=    [image]
           if not all(isinstance(__snake_case  ,(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
               raise ValueError(
                   F'''Input is in incorrect format: {[type(__snake_case   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''   )

           lowercase__									=    torch.cat([prepare_image(__snake_case  ,__snake_case  ,__snake_case   ) for i in image]  ,dim=0   )
           lowercase__									=    image.to(dtype=image_embeds.dtype  ,device=__snake_case   )

           lowercase__									=    self.movq.encode(__snake_case   )['latents']
           lowercase__									=    latents.repeat_interleave(__snake_case  ,dim=0   )
           self.scheduler.set_timesteps(__snake_case  ,device=__snake_case   )
           lowercase__									=    self.get_timesteps(__snake_case  ,__snake_case  ,__snake_case   )
           lowercase__									=    timesteps[:1].repeat(batch_size * num_images_per_prompt   )
           lowercase__									=    downscale_height_and_width(__snake_case  ,__snake_case  ,self.movq_scale_factor   )
           lowercase__									=    self.prepare_latents(
               __snake_case  ,__snake_case  ,__snake_case  ,__snake_case  ,image_embeds.dtype  ,__snake_case  ,__snake_case   )
           for i, t in enumerate(self.progress_bar(__snake_case   )   ):
               # expand the latents if we are doing classifier free guidance
               lowercase__									=    torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

               lowercase__									=    {'image_embeds': image_embeds}
               lowercase__									=    self.unet(
                   sample=__snake_case  ,timestep=__snake_case  ,encoder_hidden_states=__snake_case  ,added_cond_kwargs=__snake_case  ,return_dict=__snake_case  ,)[0]

               if do_classifier_free_guidance:
                   lowercase__									=    noise_pred.split(latents.shape[1]  ,dim=1   )
                   lowercase__									=    noise_pred.chunk(2   )
                   lowercase__									=    variance_pred.chunk(2   )
                   lowercase__									=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   lowercase__									=    torch.cat([noise_pred, variance_pred_text]  ,dim=1   )

               if not (
                   hasattr(self.scheduler.config  ,"variance_type"   )
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                   lowercase__									=    noise_pred.split(latents.shape[1]  ,dim=1   )

               # compute the previous noisy sample x_t -> x_t-1
               lowercase__									=    self.scheduler.step(
                   __snake_case  ,__snake_case  ,__snake_case  ,generator=__snake_case  ,)[0]

           # post-processing
           lowercase__									=    self.movq.decode(__snake_case  ,force_not_quantize=__snake_case   )['sample']

           if output_type not in ["pt", "np", "pil"]:
               raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''   )

           if output_type in ["np", "pil"]:
               lowercase__									=    image * 0.5 + 0.5
               lowercase__									=    image.clamp(0  ,1   )
               lowercase__									=    image.cpu().permute(0  ,2  ,3  ,1   ).float().numpy()

           if output_type == "pil":
               lowercase__									=    self.numpy_to_pil(__snake_case   )

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__snake_case   )









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    lowercase__					=       (
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    lowercase__					=       """document_qa"""
    lowercase__					=       AutoProcessor
    lowercase__					=       VisionEncoderDecoderModel

    lowercase__					=       ["""image""", """text"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    str		,     *__snake_case					:    Any		,     **__snake_case					:    List[Any]      ):
      if not is_vision_available():
        raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'      )

      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    "Image"		,     __snake_case					:    str      ):
      a		:   Union[str, Any]	  =	'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
      a		:   Any	  =	task_prompt.replace('{user_input}'		,     __snake_case      )
      a		:   Dict	  =	self.pre_processor.tokenizer(
          __snake_case		,     add_special_tokens=__snake_case		,     return_tensors='pt'      ).input_ids
      a		:   Tuple	  =	self.pre_processor(__snake_case		,     return_tensors='pt'      ).pixel_values

      return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







    def      lowercase_			( self					:    Any		,     __snake_case					:    int      ):
      return self.model.generate(
          inputs['pixel_values'].to(self.device      )		,     decoder_input_ids=inputs['decoder_input_ids'].to(self.device      )		,     max_length=self.model.decoder.config.max_position_embeddings		,     early_stopping=__snake_case		,     pad_token_id=self.pre_processor.tokenizer.pad_token_id		,     eos_token_id=self.pre_processor.tokenizer.eos_token_id		,     use_cache=__snake_case		,     num_beams=1		,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,     return_dict_in_generate=__snake_case		,     ).sequences







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Tuple      ):
      a		:   str	  =	self.pre_processor.batch_decode(__snake_case      )[0]
      a		:   str	  =	sequence.replace(self.pre_processor.tokenizer.eos_token		,     ''      )
      a		:   Union[str, Any]	  =	sequence.replace(self.pre_processor.tokenizer.pad_token		,     ''      )
      a		:   List[str]	  =	re.sub(r'<.*?>'		,     ''		,     __snake_case		,     count=1      ).strip()  # remove first task start token
      a		:   Dict	  =	self.pre_processor.tokenajson(__snake_case      )

      return sequence["answer"]